Concept-based explainable AI is promising as a tool to improve the understanding of complex models at the premises of a given user, viz.\ as a tool for personalized explainability. An important class of concept-based explainability methods is constructed with empirically defined concepts, indirectly defined through a set of positive and negative examples, as in the TCAV approach (Kim et al., 2018). While it is appealing to the user to avoid formal definitions of concepts and their operationalization, it can be challenging to establish relevant concept datasets. Here, we address this challenge using general knowledge graphs (such as, e.g., Wikidata or WordNet) for comprehensive concept definition and present a workflow for user-driven data collection in both text and image domains. The concepts derived from knowledge graphs are defined interactively, providing an opportunity for personalization and ensuring that the concepts reflect the user's intentions. We test the retrieved concept datasets on two concept-based explainability methods, namely concept activation vectors (CAVs) and concept activation regions (CARs) (Crabbe and van der Schaar, 2022). We show that CAVs and CARs based on these empirical concept datasets provide robust and accurate explanations. Importantly, we also find good alignment between the models' representations of concepts and the structure of knowledge graphs, i.e., human representations. This supports our conclusion that knowledge graph-based concepts are relevant for XAI.

Machine learning is one of the most significant developments in the field of artificial intelligence. It is a method of teaching machines to learn from data, without being explicitly programmed. In other words, it is about creating algorithms that enable machines to recognize patterns in data and make decisions based on that data. In this blog post, we'll explore the main concepts of machine learning and why it is such a powerful tool in today's world. Supervised learning is the most common type of machine learning.

Machine learning books are a great resource to pump up your knowledge, and in our experience usually explain things better and deeper than online courses or MOOCs. Once you are comfortable with Python and with Data Analysis using its main libraries, it is time to enter the fantastic world of Machine Learning: Predictive models, applications, algorithms, and much more. There are a lot of books out there that try to teach you Machine Learning; here we have only listed some of the best ones. Before getting into more extensive coding ML books, we wanted to offer a book that is more related towards giving the readers an understanding of the main topics of Machine Learning and artificial intelligence in an elegant, clear, and concise manner. Although there is code and maths in the book, the goal of the 100 Page Machine Learning book by Andriy Burkov is to provide a common ground for any kind of person with an STEM background to meet the wonderful world of Data Science. It covers an amazing variety of topics but not in the depth that might be offered by other books (take into account it is only a little more than 100 pages), but it does so in a simple and clear manner, and it is useful for Machine Learning practitioners as well as for newcomers to the field.

Imagine you are teaching a kid to differentiate dogs from cats: at first, you show him many images of both animals, identifying each of them. With these examples, he can associate each animal with its name and then classify new images correctly. The supervised learning has exactly the same idea: from a big train dataset, the algorithm "learns" the relationship between data and label and, therefore, it can predict the result of any other input. In mathematical terms, we are trying to find a expression Y f(X) b that can predict the results. Where X is the input, Y is the prediction and f(X) b is the model learned by the algorithm.

Embeddings are one of today's main concepts in machine learning. Learn how to visualize, train and apply embeddings for text and image data. Embeddings are one of today's main concepts in machine learning. They capture the semantics of all kinds of data, such as texts, images and videos. Image classification and sentiment analysis are only two out of many applications.

Attempts to use artificial intelligence (AI) technology in industry settings often fail to identify "how" AI will help a user make a better decision. For example, I have seen recommender systems developed without considering how the users' actions can improve future recommendations. If users can't see the value of their interactions, they may choose not to use the system. To address this challenge, I created a four-step process to help improve the success of AI applications. These steps can help users define the parts of their AI solution through brainstorming, discussion and iteration.

This is a concise and accessible introduction to the field of game theory. The audience for game theory has drastically expanded and now is used in diverse disciplines such as political science, biology, psychology, economics, linguistics, sociology, and computer science. The book covers the main classes of games, their representations, and the main concepts used to analyze them. ISBN 9781598295931, 88 pages.